Focus on SMEs experiments: Contextualized Vision System for Error Reduction

WHAT ABOUT THE EXPERIMENT? 

 

Under the AI REDGIO 5.0 project, CAP Engineering and Control 2K are jointly leading this experiment designed to improve quality assurance in automated manufacturing by creating a real-time, context-aware monitoring system that combines data from machines, vision systems, human operators, and safety tools. 

The system captures and processes data from various sources, including PLCs, sensors, cameras, and human input, integrating it into a unified platform to support smarter decision-making. At the heart of this is Control 2K’s Industreweb-X™ data platform, which enables advanced visualisation, AI integration, and real time performance monitoring. By providing insights into where and why errors occur, the system helps manufacturers reduce defects and improve production reliability. 

Initially developed to support quality control in a fast-paced, recently automated process, the experiment has since evolved in response to technological advancements. The focus has shifted from simply adding AI to vision systems now common in the market to building a holistic data architecture that supports human-machine collaboration and context-rich analytics. 

 

WHAT IS EXPECTED? 

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The experiment aims to deliver a more intelligent and adaptive quality management system that not only matches the precision of manual inspection but does so at the speed of automated production. Through the fusion of AI, contextual data, and human oversight, CAP Engineering and Control 2K are working to set a new standard for safe, efficient, and explainable manufacturing systems. The outcomes will serve as a valuable reference for other SMEs looking to adopt intelligent automation in their production lines.